The annotation guidelines and the theory that underlies it are less explicit on the principles underlying this task than they are on the other subtasks.

the accuracy of the current system drops considerably when this task is taken into account, from 97.5% F 1 to 86.9% (see Table 7).


Figure 3 shows Logic C. Parser C is any parser based on Logic C. As in Melamed (2003) .

the Goal item must be contiguous in all dimensions.


For the unstructured components, there are no useful nonterminal labels.

we assume that the unstructured components use only one (dummy) nonterminal label , so that and undefined otherwise for § .


First, we can take advantage of past and future research on making parsers more accurate and more efficient.

second, we can concentrate our efforts on better models, without worrying about MT-specific search algorithms.


The binarized productions are as follows: 's in their productions.

gCNF is a more restrictive normal form than those used by Wu (1997) and Melamed (2003).


Previous research has demonstrated that part of speech and frequency, or a combination of these two, are very reliable predictors of pitch accent.

to test the worthiness of using a CRF model, the first experiment we ran was a comparison of an HMM to a CRF using just the combination of part of speech and unigram.


More importantly, although it is a statistical theory, it has been shown to mimic many aspects of human competence / performance (Landauer and Dumais, 1997).

it appears to capture important components of meaning.


his target classification, and the reported baseline of picking the most frequent DA appears compatible with both CallHome37 and CallHome10.

5 our results with FLSA are slightly worse (-1.33%) or better (+ 2.68%) than Ries', depending on the target classification.


For example, training each FLSA model on CallHome37 takes about 35 minutes of CPU time, and on Map-Task 17 minutes, on computers with one Pentium 1.7Ghz processor and 1Gb of memory.

it would be better to focus only on the most promising models, especially when the number of features is high, because of the exponential number of combinations.


Instead it performs better than humans on distinguishing check and query-yn.

most of the sources of confusion for humans are the same as for FLSA.


As we will see, our starting performance is already high.

the additional information discovered by SVD-ASO appears crucial to achieve appreciable improvements.


However, even with these efficient methods, training can be slow.

most of the tasks to which CRFs have been applied are relatively small scale, having only a small number of training examples and small label sets.


In general, this work has demonstrated the susceptibility of CRFs to overfit the training data during parameter estimation.

it is now standard to use some form of overfitting reduction in CRF training.


These advanced methods tend to be difficult to implement and are often computationally expensive.

due to its ease of implementation, the current standard approach to reducing overfitting in CRFs is the use of a prior distribution over the model parameters, typically a Gaussian.


Indeed, the resulting LOP is itself a CRF, the LOP-CRF, with potential functions given by a log-linear combination of the potential functions of the experts, with weights w α .

of this, the normalisation constant for the LOP-CRF can be calculated efficiently via the usual forward-backward algorithm for CRFs.


Our goal in developing DIAG-NLP1 and DIAG-NLP2 was to assess whether simple, rapidly deployable NLG techniques would lead to measurable improvements in the student's learning.

in both cases it is still DIAG that performs content determination, and provides to DIAG-NLP1 and DIAG-NLP2 a file in which the facts to be communicated are written -a fact is the basic unit of information that underlies each of the clauses in a reply by DIAG-orig.


As the annotation was performed by a single coder, we lack measures of intercoder reliability.

what follows should be taken as observations rather than as rigorous findings -useful observations they clearly are, since sible.


Our data collection attempts at providing a more solid empirical base for aggregation rules; we found that tutors exclude significant amounts of factual information, and use high degrees of aggregation based on functionality.

while part of our rules implement standard types of aggregation, such as conjunction via shared participants, we also introduced functional aggregation (see conceptual aggregation (Reape and Mellish, 1998)).


We do not assume any structured parametrizations in is itself a parameter to be tuned.

an annotation symbol, say, F , generally does not express any commonalities among the complete non-terminals annotated by F , such as where is the label of the root node of 8 @ 9A C B and @ ¬ h ® denotes the multiset of annotated CFG rules used in the generation of 8 @ 9A C B .


A large treebank can be used as training and test data for statistical models.

we now have the basis for the development and the evaluation of statistical disambiguation models for wide-coverage HPSG parsing.


Yet, they can be parsed in O(n 3 ) time (Eisner, 1996).

dependency parsing is a potential "sweet spot" that deserves investigation.


We define the loss of a dependency tree as the number of words that have the incorrect parent.

the largest loss a dependency tree can have is the length of the sentence.


Then, feature weights are recalculated, boosting the weights of features that characterize many of the words that are most similar to the given one 2 .

the most prominent features of a word are concentrated within the top-100 entries of the vector.


Label difference: 1 2 3 Three-class data 37% 33% -Four-class data 34% 31% 30% shows that in aggregate, the vocabularies of distant classes overlap to a degree surprisingly similar to that of the vocabularies of nearby classes.

item similarity as measured by TO(cos) may not correlate well with similarity of the item's true labels.


However, the three-class results for metric labeling on top of OVA and regression (shown in Figure 2 by black versions of the corresponding icons) show that employing explicit similarities always improves results, often to a significant degree, and yields the best overall accuracies.

we can in fact effectively exploit similarities in the three-class case.


Intuitively, the state of the lexical network is locally polarized.

we calculate values of m with several different values of β and select the value just before the phase transition.


We extracted the words tagged with "Positiv" or "Negativ", and reduced multiple-entry words to single entries.

we obtained 3596 words (1616 positive words and 1980 negative words) 1 .


Our model uses only the tendency of having the same orientation.

we can extract semantic orientations of new words that are not listed in a dictionary.


Without loss of generality, we assume j > k. The goal of the training procedure is to find a parameter vector w that yields a "ranking score" function w • Φ(x i j ), which minimizes the number of violations of pairwise rankings provided in the training set.

the ideal w would satisfy the condition w The problem is typically treated as a Support Vector Machine constraint optimization problem, and can be solved using the search technique described in Joachims (2002a).


The Kullback-Leibler (KL) divergence function is a distributional similarity function that is of particular relevance here since it can be described informally in terms of substitutability.

given cooccurrence distributions p and q, its mathematical definition can be written as:  (e) w1 and w2 are EXCLUSIVE Figure 3: Surprise in substituting w 2 for w 1 (darker shading indicates higher surprise) The value log 1 p(x) has an informal interpretation as a measure of how surprised an observer would be to see event x, given prior likelihood expectations defined by p. if p and q are the distributions of words w 1 and w 2 then where E p is the expectation function over the distribution of w 1 (i.e.


This is illustrated using light and dark shading in Figure 3d.


Further, the nth-best parse can only involve at most n suboptimal parsing decisions, and all but one of these must be involved in one of the second through the n − 1th-best parses.

the basic idea behind this approach to n-best parsing is to first find the best parse, then find the second-best parse, then the third-best, and so on.


1 Second, the kernel is defined using the trained parameters of the probabilistic model.

the kernel is in part determined by the training data, and is automatically tailored to reflect properties of parse trees which are relevant to parsing.


Larger trees are usually less effective to discrimination.

we give a size threshold s, and use subtrees whose size is no greater than s. This constraint is easily realized by controlling the rightmost extension according to the size of the trees.


To select the top five answers, it is necessary to loosen the condition for extracting answers.

in the execution phase, we only give label O to a word if its probability exceeds 99%, otherwise we give the second most probable label.


By perceptual tasks we mean tasks that can be performed by simply viewing the graphic, such as finding the top of a bar in a bar chart; by cognitive tasks we mean tasks that are done via mental computations, such as computing the difference between two numbers.

one source of evidence about the intended message is the relative difficulty of the perceptual tasks that the viewer would need to perform in order to recognize the message.


Our overall accuracy without the caption evidence was 64.5%, while the inclusion of caption evidence increased accuracy to 79.1% for an absolute increase in accuracy of 14.6% and a relative improvement of 22.6% over the system's accuracy without caption evidence.

we conclude that shallow processing of a caption provides evidence that can be effectively utilized in a Bayesian network to recognize the intended message of an information graphic.


• Most of the CRs point out one specific element (with only a minority being independent as shown in Table 5).

in task-oriented dialogues, CRs locate the understanding problem directly and give partial credit for what was understood.


We would further need to retain the subphrase alignment s in order to know the correspondence between the words (i, j) ∈ s in the aligned phrases, and the total number of foreign words that each e i is aligned with (|{i|(i, j) ∈ s}|).

since a phrase alignment <f ,ē > may have multiple possible word-level alignments, we retain a set of alignments s and take the maximum: our suffix array-based data structure can be used straightforwardly to look up all aligned translations for a given phrase and calculate the probabilities on-the-fly.


At each step, two coindexed nonterminals are rewritten using the two components of a single rule, such that none of the newly introduced symbols is linked to any symbols already present.

the hierarchical phrase pairs from our above example could be formalized in a synchronous CFG as: where we have used boxed indices to indicate which occurrences of X are linked by ∼.


The parser only operates on the French-side grammar; the English-side grammar affects parsing only by increasing the effective grammar size, because there may be multiple rules with the same French side but different English sides, and also because intersecting the language model with the English-side grammar introduces many states into the nonterminal alphabet, which are projected over to the French side.

our decoder's search space is many times larger than a monolingual parser's would be.


For instance, using fewer, larger treelet translation pairs often provides better translations, since they capture more context and allow fewer possibilities for search and model error.

we add a feature function that counts the number of phrases used.


Converting this exhaustive search to dynamic programming relies on the observation that scoring a translation candidate at a node depends on the following information from its descendents: the order model requires features from the root of a translated subtree, and the target language model is affected by the first and last two words in each subtree.

we need to keep the best scoring translation candidate for a given subtree for each combination of (head, leading bigram, trailing bigram), which is, in the worst case, O(V 5 ), where V is the vocabulary size.


Of course, the quality ranking provided by QUEEN is meaningless if the similarity metric x does not capture the essential features of the models.

we need to estimate the quality of similarity metrics in order to use QUEEN effectively.


K&M did not have a true syntax-based language model to use as we have.

they divided the language model into two parts.


Due to the complex structure of the dialogue, the summary itself exhibits some discourse structure, necessitating such reader guidance phrases such as "for the … question," "on the … subject," "regarding …," "later in the same thread," etc., to direct and refocus the reader's attention.

further manual editing and partitioning is needed to transform a multi-topic digest into several smaller subtopic-based gold-standard reference summaries (see Section 6.1 for the transformation).


First, we look at each message and assume that each participant responds to an ongoing discussion by stating his/her opinion on several topics or issues that have been discussed in the current chat log, but not necessarily in the order they were discussed.

topic shifts can occur sequentially within a message.


When choosing an appropriate clustering method, because the number of subtopics under discussion is unknown, we cannot make an assumption about the total number of resulting clusters.

nonhierarchical partitioning methods cannot be used, and we must use a hierarchical method.


We start with Collins' Model 1, which lexicalizes a PCFG by associating a word w and a POS tag t with each non-terminal X in the tree.

a non-terminal is written as X(x) where x = w,t and X is constituent label.


The addition of lexical heads leads to an enormous number of potential rules, making direct estimation of P(RHS|LHS) infeasible because of sparse data.

the generation of the RHS of a rule given the LHS is decomposed into three steps: first the head is generated, then the left and right sisters are generated by independent 0th-order Markov processes.


However, in this paper we are concerned with the more realistic problem of accepting text as input.

the parser also needs a probability distribution P w ¡ w¢ LHS£ to generate words.


This tagger was developed with German in mind, but neither it nor any other advanced POS tagger morphology analyzer has ever been tested with a full parser.

we take the novel step of integrating this suffix analyzer into the parser for the second P w distribu-tion.


For languages with relatively free constituent order, such as German, Dutch, or the Slavic languages, the combinatorial potential of the language encoded in constituency cannot be mapped straightforwardly onto the word order possibilities of those languages.

the treebanks that have been created for German (NEGRA, Skut et al., 1997;VERBMOBIL, Hinrichs et al., 2000;TIGER, Brants et al., 2002) have relaxed the requirement that constituents have to be contiguous.


The filter for unwanted NIL strings described in the previous section eliminates the NIL value from consideration.

there is no variation and no tractability problem in constructing n-grams.


Due to the nature of discriminants, having two positively marked but competing discriminants for the same word will result in no trees satisfying the conditions.

it is important that only negative discriminants should be used for more general lexical types.


In Stochastic OT, constraints are regarded as independent normal distributions with unknown means and fixed variance.

the stochastic constraint hierarchy generates systematic linguistic variation.


are obtained by repeatedly sampling the 3 normal distributions, generating the winning candidate according to the ordering of constraints, and counting the relative frequencies in the outcome.

the grammar will assign nonzero probabilities to a given set of outputs, as shown above.


Notice if we take the usual approach by using the relationship p(G|D) ∝ p(D|G) • p(G), we will encounter the same problem as in Section 2.

we need a feasible way of sampling p(G|D) without having to derive the closed-form of p(D|G).


Instead it reveals a problematic use of heuristics in GLA 12 : since the constraint B is violated by [ubita], it is always demoted whenever the underlying form /uba/ is encountered during learning.

even though the expected model assigns equal values to µ 3 and µ 4 (corresponding to D and B, respectively), µ 3 is always less than µ 4 , simply because there is more chance of penalizing D rather than B.


But as observed in 6.2, an exact fit is impossible to obtain using Stochastic OT, due to the difference in the way variation is generated by the models.

it may be seen that Max-Ent is a more powerful class of models than Stochastic OT, though it is not clear how the Max-Ent model's descriptive power is related to generative linguistic theories like phonology.


It is worth pointing out that the Graduate Learning Algorithm can also be seen from this perspective.

an examination of the GLa shows that when the plasticity term is fixed, parameters found by GLa also form a Markov chain G (t)  assuming the model is identifiable, it seems possible to use GLa in the same way as the MCMC methods: rather than forcing it to stop, we can run GLa until it reaches stationary distribution, if it exists.


Gibbs sampling defines a Markov chain in the space of possible variable assignments (in this case, hidden state sequences) such that the stationary distribution of the Markov chain is the joint distribution over the variables.

it is called a Markov Chain Monte Carlo (MCMC) method; see Andrieu et al.


Third, our technique employs Gibbs sampling for approximate inference, a simple and probabilistically well-founded algorithm.

of these differences, our approach is easier to understand, implement, and adapt to new applications.


Worse, as experience with part-of-speech and grammar learning has shown, induction systems are quite capable of producing some uninterpretable mix of various levels and kinds of structure.

if one is to preferentially learn one kind of inherent structure over another, there must be some way of constraining the process.


For example, "Acme Inc. fired Jones", "Acme Inc. let Jones go", and "Jones was given notice by his employers, Acme Inc." are all ways of expressing the same fact.

the generation of extraction patterns is pertinent to paraphrase identification which is central to many language processing problems.


We have found that the distribution of patterns and documents in the corpus means that learning virtually any pattern will help improve the F-measure.

we believe the sentence filtering evaluation to be more useful for this problem.


Ultimately, after all, WSD is a method of compensating for sparse data.

it may be that the present inability of WSD models to help improve accuracy of SMT systems stems not from an inherent weakness of dedicated WSD models, but rather from limitations of present-day SMT architectures.


Larger edge weights allow labels to travel through easier.

the closer the examples, more likely they have similar labels (the global consistency assumption).


In label propagation process, the soft label of each initial labeled example is clamped in each iteration to replenish label sources from these labeled data.

the labeled data act like sources to push out labels through unlabeled data.


SemCor), in which few examples for a subset of the ambiguous words in the lexicon can be found.

improving the performance of WSD systems with few learning examples is a fundamental step towards the direction of designing a WSD system that works well on real texts.


Kernel methods allow us to build a modular system, as the kernel function acts as an interface between the data and the learning algorithm.

the kernel function becomes the only domain specific module of the system, while the learning algorithm is a general purpose component.


The Domain Kernel is defined by where D is the Domain Mapping defined in equation 1.

the Domain Kernel requires a Domain Matrix D. For our experiments we acquire the matrix D LSA , described in equation 2, from a generic collection of unlabeled documents, as explained in Section 2.


In order to show the impact of using Domain Models in the supervised learning process, we defined two WSD kernels, by applying the kernel combination schema described by equation 3.

the following WSD kernels are fully specified by the list of the kernels that compose them.


This forms the basic idea of our approach.

under this scheme we can overcome errors introduced by one processing level; more particularly, we expect accurate low level information to help with less accurate deep level information.


Moreover, our current work is done when the Entity Detection and Tracking (EDT) has been perfectly done.

it would be interesting to see how imperfect EDT affects the performance in relation extraction.


In this work, we focus on lexical differences between genders on telephone conversations and use machine learning techniques applied on text categorization and feature selection to characterize these differences.

our conclusions are entirely data-driven.


However, the best results are obtained by pooling all the data and training a single language model.

despite the fact there are different modes, the benefit of more training data outweighs the benefit of gender-dependent models.


Ever increasing computing power and connectivity bandwidth together with falling storage costs result in an overwhelming amount of data of various types being produced, exchanged, and stored.

search has emerged as a key application as more and more data is being saved (Church, 2003).


e is a source language word, f is a target langauge word, and conf is a positive real-valued number (usually, conf = 1.0) assigned by lexicographers to evaluate the validity of the entry.

the feature function using a bilingual dictionary is:  We use the GIS (Generalized Iterative Scaling) algorithm (Darroch and Ratcliff, 1972) to train the model parameters λ M 1 of the log-linear models according to Eq.


Gain threshold t is a real-valued number, which can be optimized on the development corpus.

we have a new search algorithm: Input: e, f , eT, fT, D and t Output: a 1.


In the domain-specific (in-domain) corpus, there are two kinds of words: general words, which also frequently occur in the out-of-domain corpus, and domain-specific words, which only occur in the specific domain.

we can use the out-of-domain bilingual corpus to improve the alignment for general words and use the in-domain bilingual corpus for domain-specific words.


Only one-to-one and many-to-one word alignments are considered.

some multi-word units in the domain-specific corpus cannot be correctly aligned.


The same word may have different distributions in the in-domain corpus and the out-of-domain corpus.

the interpolation weight for the translation probability is taken as a variant.


The independence assumptions typifying S-CFGs are also valid for S-ITGs.

the probability of an S-ITG parse is calculated as the product of the probabilities of all the instances of rules in the parse tree.


We keep only the top-k lexicalized items for a given chart cell of a certain nonterminal Y contained in the cell l, m, i, j.

the additional complexity of O(N s N t ) will be replaced by a constant factor.


But this model expresses a text T as word sequence T=w 1 ,w 2 ,…,w n .

in this model, the probability P(L) is calculated with Formula 5 and the probability P(T|L) is calculated by Here we assume the independent occurrence of words labelled as the same information types.


A voice tokenizer converts a spoken document into a text-like document of acoustic tokens.

a spoken document can be represented by a count vector of acoustic tokens and token n-grams in the vector space.


Spoken language and written language are similar in many ways.

much of the research in spoken language identification, LID, has been inspired by text-categorization methodology.


In monolingual spoken document categorization, we suggest that the semantic domain can be characterized by latent phonotactic features.

it is straightforward to extend the proposed bag-of-sounds framework to spoken document categorization.


In practice, a teacher is likely to be looking for texts at a particular level rather than classifying a group of texts into a variety of categories.

we construct one classifier per category which decides whether a document belongs in that category or not, rather than constructing a classifier which ranks documents into different categories relative to each other.


However, our corpus is limited and preliminary experiments in which the training data was split for LM and SVM training were unsuccessful due to the small size of the resulting data sets.

we made use of the Britannica and CNN articles to train models of three n-gram orders on "child" text and "adult" text.


However, structural divergences between languages (Dorr, 1994)，which are due to either systematic differences between languages or loose translations in real corpora，pose a major challenge to syntax-based statistical MT.

the syntax based MT systems have to transduce between non-isomorphic tree structures.


This model is used directly for translation by solving the following maximization problem: Since a source language sentence is given and the È Ö Â ½ µ probability is applied to all possible corresponding target sentences, we can ignore the denominator in equation 3.

the joint probability model can be used to describe the correspondence between two sentences.


Since a target and a source word are predicted through reciprocal incorporation between source and target context from the beginning of a target sentence, the word order in the target sentence is automatically determined and the number of possible configurations of source words is decreased.

we do not need to perform any computation for word re-ordering.


Moreover, since correspondences are provided based on bilingual contextual evidence, translation ambiguities can be decreased.

the proposed model is expected to reduce computational complexity during the decoding as well as improve performance.


We use a log-linear probability model and maximum likelihood training-the parameter , then a loglinear probability model can be represented as denotes the feature vector of the corresponding block translation, and the partition function is: A disadvantage of this approach is that the summation over can be rather difficult to compute.

some sophisticated approximate inference methods are needed to carry out the computation.


8, which has the statistical interpretation of estimating the conditional probability.

it does not have the potential issues of the perceptron method which we pointed out earlier.


Unless one employs an elaborated procedure, the candidate translations may also be very similar to one another, and thus do not give a good coverage of representative translations.

the reranking approach may have some severe limitations which need to be addressed.


Given a new instance C i , "This 2000 square foot 3 bedroom, 2 bathroom house is a colonial", deleting p 1 , the main object of the verb, will make the rest of the sentence incomplete.


In contrast, English morphological tagsets usually have about 50 tags, which cover all morphological variation.

morphological disambiguation of a word in context, i.e., choosing a complete 1 We would like to thank Mona Diab for helpful discussions.


2 In this paper, we only discuss inflectional morphology.

the fact that the stem is composed of a root, a pattern, and an infix vocalism is not relevant except as it affects broken plurals and verb aspect.


Error analysis showed that some features specifically suited for one argument class, for example, core arguments, tend to hurt performance on some adjunctive arguments.

we thought that selecting subsets of features for each argument class might improve performance.


Note that we don't do any hard pruning at the identification stage in testing and can find the exact labeling of the complete parse tree, which is the maximizer of Equation 1.

we do not have accuracy loss as in the two-pass hard prune strategy described in (Pradhan et al., 2005).


A problem with this approach is that a maximizing labeling of the nodes could possibly violate the constraint that argument nodes should not overlap with each other.

to produce a consistent set of arguments with local classifiers, we must have a way of enforcing the non-overlapping constraint.


Gil & Dias 2003b) make this a relatively feasible task.

given a phrase like east end, we can rank how often __ end appears with east in comparison to how often other phrases appear with east.That is, if {__ end, __side, the __, toward the __, etc.}


This corresponds to the intersection of two half spaces, the dihedral angle between which is θ.

we Hence from equation 3 we have, This equation gives us an alternate method for finding cosine similarity.


We apply q different random permutation for every vector (by choosing random values for a and b, q number of times).

for every vector we have q different bit permutations for the original bit stream.


If the hamming distance is below a certain predetermined threshold, we output the pair of vectors with their cosine similarity (as calculated by equation 4).


[n] can thus be considered as a set of integers from 0 to n − 1.

one possible mapping is: π : {0, 1, 2, 3} → {3, 2, 1, 0} Here it means: π(0) = 3, π(1) = 2, π(2) = 1, π(3) = 0 Another possible mapping would be: π : {0, 1, 2, 3} → {3, 0, 1, 2} Here it means: π(0) = 3, π(1) = 0, π(2) = 1, π(3) = 2 for the set [4] there would be 4!


It only requires consistency between training and testing.

there is a wide range of possible preprocessing choices for data used in statistical machine translation.


An example of an orthographic rule is the deletion of the Alif (© ) of the definite article + ¡ Templatic Inflections: Some of the inflectional features in Arabic words are realized templatically by applying a different pattern to the Arabic root.

extracting the lexeme (or lemma) of an Arabic word is not always an easy task and often requires the use of a morphological analyzer.


However, when computing CPER, an incorrect or missing alignment link might result in more than one phrase pair being eliminated from or added to the set of phrases.

the impact is more severe on both precision and recall.


We realized that the publiclyavailable implementation of Pharaoh computes the lexical weights only for non-NULL alignment links.

loose phrases containing NULL-aligned words along their edges receive the same lexical weighting as tight phrases without NULL-aligned words along the edges.


The size of the phrase table increases dramatically as the number of links in the initial alignment gets smaller.

for both languages, S U and S G yield a much smaller Chinese  Arabic  Alignment MPL=3 MPL=7 MPL=3 MPL=7  SU  106  122  32  38  SG  161  181  48  55  SI  1331  3498  377  984  SA  954  1856  297  594  SB  876  1624  262  486   Table 7: Number of Phrases in the Phrase Table Filtered for MTEval '2003 Test Sets (in thousands) phrase table than the other three alignments.


Since precision-oriented alignments make a higher number of longer phrases available to the decoder (based on the coverage of phrases presented in Table 9), they are used more during decoding.

the major difference between the alignments is the coverage of the phrases extracted from different alignments.


However, computing the denominator is intractable.

we sample from the posterior distribution P (z, c|w) using Markov chain Monte Carlo (MCMC) (Gilks et al., 1996).


This criterion measures both the similarity within each partition and the dissimilarity across different partitions.

our approach moves beyond localized comparisons and takes into account long-range changes in lexical distribution.


The time delay of using an Internet search engine within a large-scale anaphora resolution effort is currently impractical.

we attempted  2005, we were only able to boost performance from an F-Score of 85.4% to one of 88.0% (Table 3).


our bootstrapping assumption was that the majority of times these paths occur, gender indicates coreference or lack thereof.

when they occur in our test sets, gender should often sufficiently indicate coreference.


Instead, "on" and "to", have different frequencies and this information is reflected in the conditional probability P (word i |state).

the slower reading time in (14b) might be attributable to the lower frequency of the disambiguating word, "to" compared to "on".


Contrary to the major claim made in previous empirical studies, which is that the garden-path phenomena are either modeled by syntactic principles or by structural frequency, the findings here show that the same phenomena can be predicted without such structural information.

the work is neither a mere extended application of Corley and Crocker's work to a broader range of data, nor does it simply confirm earlier observations that finite state machines might accurately account for psycholinguistic results to some degree.


Still, not surprisingly, a lot of the names that occur in one, also occur in the other.

(Camilla) Martin shows up in the Chinese version as ma-er-ting; Judith Meulendijks is õ yu mo-lun-di-ke-si; and Mette Sorensen is mai su-lun-sen. Several other correspondences also occur.


Names of the same entity that occur in different languages often have correlated frequency patterns due to common triggers such as a major event.

if we have comparable news articles over a sufficiently long time period, it is possible to exploit such correlations to learn the associations of names in different languages.


As we have noted, document pairs that contain lots of plausible transliteration pairs should be viewed as more plausible document pairs; at the same time, in such a situation we should also trust the putative transliteration pairs more.

these document pairs and transliteration pairs mutually "reinforce" each other, and this can be exploited to further optimize our transliteration scores by allowing transliteration pairs to propagate their scores to each other according to their co-occurrence strengths.


This table shows  core names it is 0.89.

on average, the correct answer, if it is included in our candidate list, is ranked mostly as the first one.


There are 25 such English name candidates.

the initial scores are actually slightly different from the values in Table 3.


Intuitively, we want to preserve the initial score of a pair, but add a slight boost from its neighbors.

we set α very close to 1 (0.9 and 0.95), and allow the system to perform 20 iterations.


However, high LLR scores can indicate either a positive association (i.e. )

or a negative one; and we can distinguish between them by checking whether we can split the set of cooccurring word pairs into positively and negatively associated pairs, and obtain a measure for each of the two association types.


It is truly nonparallel; as our example from Figure 1 shows, even closely related documents have few or no parallel sentence pairs.

we expect that our extraction method should perform best on this corpus.


This corpus exhibits a higher degree of parallelism than the BBC one; in particular, it contains many article pairs which are literal translations of each other.

although our subsentence extraction method should produce useful data from this corpus, we expect the sentence extraction method to be more successful.


In order to evaluate the importance of the LLR-Lex lexicon, we also performed fragment extraction experiments that do not use this lexicon, but only GIZA-Lex.

for each initial parallel corpus and each comparable corpus, we extract three datasets: FragmentExtract, SentenceExtract, and Fragment-noLLR.


Our work lies between these two directions; we attempt to discover parallelism at the level of fragments, which are longer than one word but shorter than a document.

the previous research most relevant to this paper is that aimed at mining comparable corpora for parallel sentences.


After one round of sentence extraction, the list is enriched with additional documents, and the system iterates.

they include in the search document pairs which are dissimilar.


As the semantic method outperformed the lexical overlap in the evaluations of previous Section, we decided to acquire a clustering on the entire WordNet sense inventory using this approach.

we obtained a reduction of 33.54% in the number of entries (from 60,302 to 40,079 senses) and a decrease of the polysemy degree from 3.14 to 2.09.


words with all the senses mapped to the same ODE entry), so as to clarify the real impact of properly grouped clusters.

the random setting obtained 64.56% average accuracy, while the performance when adopting our automatic clustering was 70.84% (1,025/1,447 items).


the relation subtype level in the ACE corpus.

the final hierarchical learning strategy using the class hierarchy built with the all-level hybrid approach achieves F-measure of 58.2 in F-measure, which outperforms the final flat strategy by 2.2 in Fmeasure.


Moreover, just as indicated in Figure 2, most relation subtypes in the ACE RDC 2003 corpus (arguably the largest annotated corpus in relation extraction) suffer from the lack of training examples.

a critical research in relation extraction is how to rely on semi-supervised learning approaches (e.g.


Finally, our current work is done when NER has been perfectly done.

it would be interesting to see how imperfect NER affects the performance in relation extraction.


For supervised methods, e.g., SVM, very few labeled examples are not enough to reveal the structure of each class.

they can not perform well, since the classification hyperplane was learned only from few labeled data and the coherent structure in unlabeled data was not explored when inferring class boundary.


Using polarities allows integrating the treatment of saturation in the formalism of the rules.

the processing of saturation will pilot the combination of structures during the generation processing.


But above all, the PUG formalism allows us to write separately several modules of the grammar handling various structures and to put them together in a same formalism by synchronization of the grammars, as we show with our translation of LFG.

pUGs extend unification grammars based on feature structures by allowing a greatest diversity of geometric structures and a best control of resources.


Lexicalized representations of adjectives such as easy and easier are related, so that both lexicalized and phrasal comparatives would have the same feature structure; easier would have the feature structure  Encoding derivations in the lexicon could be applicable for languages with relatively unproductive derivational phenomena, but it certainly is not possible to represent in the grammar lexicon, 2 all derived forms as lexemes for an agglutinative language like Turkish.

one needs to incorporate such derivational processes in a principled way along with the computation of the effects on derivations on the representation of the semantic information.


If we consider Figure 2 in terms of dependency relations, the adjective mavi (blue) modifies the noun renk (color) and then the derivational suffix -li (with) kicks in although the -li is attached to renk only.

the semantics of the phrase should be with(blue color), not blue with(color).


In Japanese, a clause basically contains one verb phrase.

a complex sentence or a compound sentence contains one or more clauses.


However, since some kinds of clauses are embedded in main clauses, it is fundamentally difficult to divide a monologue into clauses in one dimension .

by using a clause boundary annotation program , we approximately achieve the clause segmentation of a monologue sentence.


This result means that 93.6% (1,385/1,479) of all dependency relations are within a clause boundary unit.

the results confirmed that the assumption made by our research is valid to some extent.


The average time taken for clause boundary analysis was about 1.2 millisecond per sentence.

the time cost of performing clause boundary analysis as a preprocessing of dependency parsing can be considered small enough to disregard.


Furthermore, as the experiment described in this paper has shown the effectiveness of our technique for dependency parsing of long sentences in spoken monologues, so our technique can be expected to be effective in written language also.

we want to examine the effectiveness by conducting the parsing experiment of long sentences in written language such as newspaper articles.


In section 2.4.1, we highlighted the fact that the n-gram user simulations are not completely realistic and that this will make certain state features more or less important in learning a strategy.

had we been able to use even more realistic user simulations, including certain additional context features in the state might have enabled a greater improvement over the baseline.


This can be written as a Gibbs distribution parameterized with weights f , where g is the size of the label set.

we use the machine learning toolkit LLAMA (Haffner, 2006) to estimate the conditional distribution using maxent.


For dialog act tagging and subtask segmentation during dialog management, we need to predict incrementally since it would be unrealistic to wait for the entire dialog before decoding.

in order to train the dialog act (DA) and subtask segmentation classifiers, we use only static features from the current and left context as shown in Table 1.


However Z λ (x) never affects the decision ofŷ since Z λ (x) does not depend on y.

we can obtain the following discriminant function for CRFs: The maximum (log-)likelihood (ML) of the conditional probability p(y|x; λ) of training data {(x k , y * k )} N k=1 w.r.t.


We minimize the following loss function for the ML criterion training of CRFs: To reduce over-fitting, the Maximum a Posteriori (MAP) criterion of parameters λ, that is, arg max λ k log p(λ|y * k , x k ) ∝ k log p(y * k |x k ; λ)p(λ), is now the most widely used CRF training criterion.

we minimize the following loss function for the MAP criterion training of CRFs: There are several possible choices when selecting a prior distribution p(λ).


It is easy to extend the MCE framework to use evaluation measures other than the classification error, namely the linear combination of error rates.

it is possible to optimize directly a variety of (smoothed) evaluation measures.


One choice of continuous misclassification measure consists of substituting 'max' with 'soft-max', max k r k ≈ log k exp(r k ).

where g * = g(y * , x, λ), g = g(y, x, λ), and A = 1 |Y|−1 .


However, it has been shown (Le Roux and McDer-mott, 2005) that even simple gradient-based (firstorder) optimization methods such as GPD and (approximated) second-order methods such as Quick-Prop (Fahlman, 1988) and BFGS-based methods have yielded good experimental optimization results.

far, we have discussed the error rate version of MCE.


It is well-known that complex models exhibit high variance and perform poorly on unseen data.

some regularization methods have to be used to control the complexity of the model.


Because the task utilizes millions of features and training samples, directly optimizing the penalized lasso loss function is impossible.

two approximation methods, the boosted lasso (BLasso, Zhao and Yu 2004) and the forward stagewise linear regression (FSLR, Hastie et al.


is a piecewise constant (or step) function of λ and its gradient is undefined.

discriminative methods apply different approaches that optimize it approximately.


It scales the incremental step δ by a small constant ν, ν ∈ (0, 1).

the update of Equation 8 Empirically, it has been found that smaller values of ν lead to smaller numbers of test errors.


Secondly, when updating a feature weight, if the size of the optimal update step (computed via Equation 7) is smaller than ε, we use the optimal step to update the feature.

in our implementation BLasso does not always take a fixed step; it may take steps whose size is smaller than ε.


It has been proved that (1) it guarantees that it is safe for BLasso to start with an initial α which is the largest α that would allow an ε step away from 0 (i.e., larger α's correspond to T(λ)=0); (2) for each value of α, BLasso performs coordinate descent (i.e., reduces Ex-pLoss by updating the weight of a feature) until there is no descent step; and (3) for each step where the value of α decreases, it guarantees that the lasso loss is reduced.

it can be proved that for a finite number of features and θ = 0, the BLasso algorithm shown in Figure 2 converges to the lasso solution when ε 0.


BLasso with backward steps provides a better mechanism than F-Boosting to revise the previously chosen features to accommodate this fine level of tuning.

we observe the superior performance of BLasso at later stages as shown in our experiments.


(2002), headwords are defined as the content words of the sentence.

headword bigrams constitute a special type of skipping bigrams which can capture dependency between two words that may not be adjacent.


This is a kind of negative halo effect: create a bad impression, and you will be scored low on everything.

bereiter (2003) insists that another approach to doing better than ordinary human raters would be to use expert writers rather than expert raters.


BETSY requires the Bayesian rules, and Intelli-Metric, the AI-based rules.

the methodology limits the grader's practical utility to largescale testing operations in which such data collection is feasible.


Jess, however, uses editorials and columns taken from the Mainichi Daily News newspaper as learning models, and such models are not sufficient for learning terms used in scientific and technical fields such as computers.

we found that Jess could return a low evaluation of "content" even for an essay that responded well to the essay prompt.


This is perhaps because the Japanese language does not have a mass count distinction system similar to that of English.

it is favorable for error detection systems aiming at Japanese learners to be capable of detecting these errors.


Variance is calculated by where denotes the number of samples used for calculating the ratio.

confidence of the estimate of the conditional probability used in the proposed method is measured by 6 The feedback corpus refers to learners' essays whose errors are corrected as mentioned in Section 1.


For example, extra definite articles (e.g., *the traveling) cannot be detected even if the correct mass count distinction is given.

only a little improvement is expected in recall however much feedback corpus data become available.


Training sets of various sizes up to 45,000 pairs were then randomly extracted from the lesioned and non-lesioned pairs so that data from both sets occurred in roughly equal proportions.

the 45K data set contains approximately 22,500 lesioned examples.


Graph transformations in dependency parsing have also been used in order to recover nonprojective dependencies together with parsers that are restricted to projective dependency graphs.

nivre and nilsson (2005) improve parsing accuracy for MaltParser by projectivizing training data and applying an inverse transformation to the output of the parser, while Hall and novák (2005) apply post-processing to the output of Charniak's parser (Charniak, 2000).


the filters described below eliminate U that (1) realize semantic relations not in the ontology; (2) contain words indicating that its meaning depends on the context; (3) contain unknown words; or (4) cannot be parsed accurately.


We apply the filters, in a cascading manner, to the 18,466 sentences with semantic representations.

we obtain 512 (2.8%) mappings of (U, R, S).


We also wish to assess whether the linguistic variation of the learned mappings was greater than what we could easily have generated with a hand-crafted dictionary, or a hand-crafted dictionary augmented with aggregation operators, as in (Walker et al., 2003).

we first categorized the mappings by the patterns of the DSyntSs.


A different type of limitation is that dialogue systems need to generate utterances for information gathering whereas the mappings we obtained can only be used for information presentation.

these would have to be constructed by hand, as in current practice, or perhaps other types of corpora or resources could be utilized.


Also, as our data usually offers no frequency information, we have adopted the maximum entropy assumption, namely, that all relative frequencies are equal.

the probability of confusion of two words depends solely on their similarity distance.


For example, Levenstein distance between interested and rest is six, the same as the distance between rest and by, even though the latter two have nothing in common.

close phonetic transcriptions, which by their very nature are likely to involve more symbols per word, will result in larger edit distances than broad phonemic transcriptions of the same data.


They considered supertagging a process of almost parsing, since all that needs to be done after having a sufficiently reliable sequence of supertags available is to decide on their combination into a spanning tree for the complete sentence.

the approach lends itself easily to preprocessing sentences or filtering parsing results with the goal of guiding the parser or reducing its output ambiguity.


Unfortunately, it is difficult to measure performance on such tasks.

our experiments are based on two tasks that provide objective performance measures.


Second, 0 ) : implies that the pattern j P does not actually appear with the word pair i i Y X : in the corpus; we are only guessing that the pattern is appropriate for the word pair, and we could be wrong.

we prefer to limit ourselves to patterns and word pairs that have actually been observed in the corpus.


Weighted constraints provide an ideal interface to integrate arbitrary predictor components in a soft manner.

external predictions are treated the same way as grammar-internal preferences, e.g.


But this model, used with the ranking according to , leads results that are very similar to those obtained without smoothing.

we describe the smoothingless model, which has the advantage not to use an empirically chosen smoothing function.


But FRMG and SXLFG use completely different parsers, that rely on different formalisms, on different grammars and on different parser builder.

the comparison of error mining results on the output of these two systems makes it possible to distinguish errors coming from the Lefff or from SXPipe from those coming to one grammar or the other.


As in (Krippendorff, 1980), we assume there exists one global distribution for our task (i.e., the duration ranges for all the events), and "chance" annotations would be consistent with this distribution.

the baseline will be an annotator who knows the global distribution and annotates in accordance with it, but does not read the specific article being annotated.


we note that T 3 is structurally (and somehow lexically similar) to T 1 and H 3 is more similar to H 1 than to H 2 .

from T 1 ⇒ H 1 we may extract rules to derive that T 3 ⇒ H 3 .


The drawback is that the solution may be only a local optimum.


and are small in practice (see (Koller and Thater, 2005b) for an analysis).

the chart can still serve as an underspecified representation.


For our first model, this context is the previous sentence.

the model can be said to capture the degree to which rule use is primed between sentences.


At this point, it suffices to say that we require a parser which has the prefix property, i.e., which parses incrementally, from left to right.

we use an Earley-style probabilistic parser, which outputs Viterbi parses (Stolcke, 1995).


Because the Copy model posits coordinated structures whenever POS tags match, parsing efficiency decreases if POS tags are not predetermined.

we assume POS tags as input, using the gold-standard tags from the treebank (following, e.g., Roark and Johnson 1999).


it assumes that long rewrites are not decomposable into smaller steps).

a variety of techniques have been developed to both enrich and generalize the naive grammar, ranging from simple tree annotation and symbol splitting (Johnson, 1998;Klein and Manning, 2003) to full lexicalization and intricate smoothing (Collins, 1999;Charniak, 2000).


At the same time, each bin gives a less robust estimate of the grammar probabilities, leading to overfitting.

it would be to our advantage to split the latent annotations only where needed, rather than splitting them all as in Matsuzaki et al.


In fact, extra subsymbols may need to be added to several nonterminals before they can cooperate to pass information along the parse tree.

we go in the opposite direction; that is, we split every symbol in two, train, and then measure for each annotation the loss in likelihood incurred when removing it.


It is well known that obtaining annotated training examples for WSD tasks is very expensive, and is often considered infeasible in unrestricted domains.

many researchers investigated unsupervised methods, which do not require annotated examples.


State-of-the-art unsupervised WSD systems are quite complex and they are not easy to be replicated.

we implemented the unsupervised version of the Lesk algorithm (Lesk, 1986) as a reference system, since it is considered a standard simple baseline for unsupervised approaches.


Compared with supervised methods, unsupervised methods do not require tagged corpus, but the precision is usually lower than that of the supervised methods.

knowledge acquisition is critical to WSD methods.


This artificial ambiguous word need to simulate the function of the real ambiguous word, and to acquire semantic knowledge as the real ambiguous word does.

we call it an equivalent pseudoword (EP) for its equivalence with the real ambiguous word.


P (s|λ) can be calculated efficiently using the forward-backward procedure which makes the estimate for the total probability of all state paths that go through filler states straightforward to be: Now it is clear to see that, if the calculated P (s, Q f iller |λ) > P (s, Q bg |λ), then segment s is considered more likely to have filler occurrence(s).

in this case we classify s as an extraction relevant segment and it will be retrieved.


In Sections 02-21 of the Penn-II Treebank, these are used more often in relative clause constructions than in questions.

many of the corpus questions were given syntactic analyses corresponding to relative clauses (SBAR with an embedded S) instead of as questions (SBARQ with an embedded SQ).


However, many corpora (Böhomvá et al., 2003;Skut et al., 1997;Brants et al., 2002) use dependency graphs or other representations, and the extraction algorithms that have been developed for Penn Treebank style corpora may not be immediately applicable to this representation.

research on statistical parsing with "deep" grammars has largely been confined to English.


One constraint was that the only alignment patterns allowed were 1-1, 1-2, 1-3, 2-1, and 3-1.

many-to-many link patterns were disallowed, and a single word could be linked to at most three other words.


Since we randomize the order of the data on every pass, if we make a pass through the training data, and the training set AER increases, it may be that we simply encountered an unfortunate ordering of the data.

when training set AER increases, we retry two additional times with the same initial weights, but different random orderings of the data, before giving up and trying a smaller learning rate.


Then the lexical rule (3) 2 is 2 Currently, we restrict phrases x and y not to be null.

neither deletion nor insertion is carried out during decoding.


This is a problem for MaxEnt parameter estimation if it is scaled to large bitexts.

for the integration of MaxEnt-based phrase reordering model in the system trained on large bitexts, we remove collocation features and only use lexical features from the last words of blocks (similar to those from the first words of blocks with similar performance).


So far, our distortion cost is defined in terms of words, not phrases.

we need to generalize the distortion cost in order to use it in a phrasebased decoder.


Last, there are more bigram features than word features because bigrams can overlap each other in the text but words can not.

the bigrams as a whole should theoretically contain more information than the words as a whole.


On the other hand, most of the proper nouns, relevant entities and words that are not yet lexicalized in the language, are expressed by using their original terms.

the same entities will be denoted with the same words in different languages, allowing us to automatically detect couples of translation pairs just by looking at the word shape (Koehn and Knight, 2002).


This implies that the synset index structure is the same for the two languages.

for the all the monosemic words, we augment each text in the dataset with the corresponding synset-id, which act as an expansion of the "common" terms of the matrix in Figure 1.


Among the common lemmata, 97% are nouns and most of them are proper nouns.

the initial term-by-document matrix is a 43,384 × 45,132 matrix, while the D LSA was acquired using 400 dimensions.


In particular, 4 and p have f-scores of about 70% and 60% respectively (Wong et al., 2005).

in our experiments, we also take prosody confidence scores into consideration.


To test hypothesis 3, we are constrained to the feature spaces that both PFS and SGC algorithms can process.

we take all the variables from Zhang and Weng (2005) as the feature space for the experiments.


The algorithm for FST based vowel restoration could not be simpler: between every pair of characters we insert diacritics if doing so improves the likelihood of the sequence as scored by a statistical n-gram model trained upon the training corpus.

in between every pair of characters we propose and score all possible diacritical insertions.


This indicates that result re-ranking fails to do its best without query expansion, since the relevant documents in original query are limited, and only the re-ranking method alone cannot solve the "relevant documents sparseness" problem.

the query expansion method, which can provide fresh and relevant documents, can help the re-ranking method to reach an even better performance.


However, their work differs from ours in three ways: First, they use the cosine similarity to implement query expansion, and use Rocchio formulation (J. J. Rocchio, 1971) to re-rank the search results.

their query expansion and re-ranking are computed separately and are not so concise and collaborative.


Thirdly, they do not compute the relevance of search results and the relativity of expanded terms in an iterative fashion.

their approach does not utilize the relation among search results, among expanded terms, and between search results and expanded terms.


This is because retrieval systems are typically more tolerant of syntactic than semantic translation errors (Fluhr, 1997).

although syntactic errors caused by the degradation of the rule base result in a decrease of translation quality, they have smaller impacts on retrieval effectiveness than the word translation errors caused by the degradation of dictionary.


This is because the MT system translates the desc queries term by term, so degradation of rule base mainly results in word translation errors instead of syntactic errors.

degradation of dictionary and rule base has similar effect on retrieval effectiveness.


Our observation showed that the size of the dictionary has a stronger effect on retrieval effectiveness than the size of the rule base in MT-based CLIR.

in order to improve the retrieval effectiveness of a MT-based CLIR application, it is more effective to develop a larger dictionary than to develop more rules.


In addition to resolving the discrepancy between event spaces, the foundational theories in information science, such as Zipf's Law (Zipf, 1949) and Shannon's Theory (Shannon, 1948) consider only a term event space.

establishing a direct connection between the empirically successful IDF and the theoretically based ITF may enable a connection to previously adopted information theories.


The small number of documents is from the Cranfield corpus in Sparck Jones (1972), medium is 100,000 from our corpus, and large is 1 million As a document corpus becomes sufficiently large, the rate of new terms in the vocabulary decreases.

in practice the rate of growth on the x-axis of Figure 1 will slow as the corpus size increases.


The major difference is that we model the syntax of the source language instead of the target side.

the task of our decoder is to find the best target string while Galley's is to seek the most likely target tree.


To further decompose P r(S|T ), the tree-tostring alignment template, denoted by the variable z, is introduced as a hidden variable.

the TAT-based translation model can be decomposed into four sub-models: 3.


In the experiments, we removed from the above data set the zero-pronouns to which the two annotators did not agree.

the data set contained 995 intra-sentential anaphoric zero-pronouns, 754 inter-sentential anaphoric zero-pronouns, and 603 non-anaphoric zeropronouns (2,352 zero-pronouns in total), with each anaphoric zero-pronoun annotated to be linked to its antecedent.


In addition, the wrong word spacing would result in the incorrect index for terms in information retrieval.

correcting the sentences with incorrect word spacing is a critical task in Korean information processing.


In addition, its performance is mainly dependent on the relevance of knowledge sources, since the prior knowledge on the target problem is very important (Park and Zhang, 2002).

when prior knowledge is not clear and computational cost is an important factor, Ò-gram models are more suitable than a maximum entropy model.


According to the well-known curse of dimensionality, the data density required is reduced exponentially by reducing dimensions.

if the lower dimensional model is not different so much from the higher dimensional one, it is highly possible that the probabilities from lower dimensional space are more stable than those from higher dimensional space.


The proposed method considers the various number of previous syllables, whereas the normal HMM has the fixed context.

the proposed method in Table 4 is specified as 'self-organizing HMM.'


Even though the normal HMM considers more previous tags ( ¾), the accuracy of the self-organizing model is 2.34% higher than that of the normal HMM.

the proposed method that considers the sequence of word spacing tags achieves higher accuracy than any other methods reported ever.


Especially, it is one of the successful language models, which is a key technique in language and speech processing.

the proposed method can be applied to not only word spacing but also many other tasks.


Realizing that the number of words, n , available in the window size, k , is generally larger than the predefined maximum length of candidate, m , it is unreasonable to use all adjacent sequential combinations of available words within the window size k .


The English key phrase for which we are looking for the translation sometimes contains several words that may appear in a dictionary as an independent unit.

it can only be partially matched based on the phonetic similarity, and the rest part may be matched by the semantic similarity in such situation.


Because the difference between Korean/Chinese and English phonetic systems makes the above uni-gram model almost impractical in terms of output quality, bi-grams are applied to substitute the single alphabet in the above equation.

the phonetic weight should be calculated as: is computed from the training corpus as the ratio between the frequency of 1 j j c c + in the candidates, which were originated from 1 j j g g + in English words, to the frequency of + is substituted with a space marker.


But the Hindi translation of "black horses" is kaale ghode; whereas "black mare" is translated as kaalii ghodii.

the same English word "black" may have three Hindi equivalents kaalaa, kaalii, and kale which are to be used judiciously by considering the number and gender of the noun concerned.


On the other hand, Lexicon-based approaches are highly resource-dependent.

they could not be considered as suitable schemes.


At the same time, compiling dictionaries is expensive, because it relies on human introspection and supervision.

a number of automatic methods have been proposed to extract loanwords and their translations from corpora, targeting various languages.


To the best of our knowledge, no attempt has been made to extract loanwords and their translations targeting Mongolian.

we will discuss existing methods targeting other languages.


Then, we compute the phonetic similarities between each candidate loanword and each retrieved Katakana word using DP matching, and select a pair whose score is above a predefined threshold.

we can extract loanwords in Mongolian and their translations in Japanese simultaneously.


Loanwords are also inflected in all of these types, except for phrase (b).

we have to identify the original form of a content word using stemming.


However, in phrase (b), a consonant insertion has occurred.

both the inserted consonant, "г", and the suffix have to be removed.


The first characters of proper nouns, such as "Эрдэнэбат (Erdenebat)", and all the characters of abbreviations, such as "ЦШНИ (Nuclear research centre)", are written using capital letters in Mongolian.

we discard words that are written using capital characters, except those occurring at the beginning of sentences.


In a modern Mongolian dictionary (Ozawa, 2000), there are 49 words beginning with "р", of which only four words are conventional Mongolian words.

a word beginning with "р" is probably a loanword.


In the row "Both" shown in Table 5, 151 loanwords were extracted, for each of which we selected up to the top five Katakana words whose similarity computed using Equation (1) was greater than 0.6 as   translations.

japanese translations were extracted for 109 loanwords.


Their application -indexing for an information retrieval system -does not require all of the morphological attributes but only the lemma and the PoS of each word.

for this case, 75% of the words remain with one analysis with 95% accuracy, 20% with two analyses and 5% with three analyses.


Only the contextually correct analysis has been hand-checked.

our quantitative analysis in Section 8.3 leaves open the question of how good the gold standard is in the first place.


But for the real-valued model, evidence supporting 1 ≤ x < 2 and 3 ≤ x < 4 provides evidence for the missing bin.

the real-valued model generalises more effectively.


With this method, all the occurrences of the unknown words in a document 1 are taken into consideration at once, rather than that each occurrence of the words is processed separately.

the method models the whole document and finds a set of parts-of-speech by maximizing its conditional joint probability given the document, rather than independently maximizing the probability of each part-of-speech given each sentence.


We assume that POS tags of known words are already determined beforehand, and positions in the document where unknown words appear are also identified.

we focus only on prediction of the POS tags of unknown words.


Although HMMs, MEMMs, and CRFs use dynamic programming and some studies with probabilistic models which have specific structures use efficient algorithms (Wang et al., 2005), such methods cannot be applied here because we are considering interactions (dependencies) between all POS tags, and their joint distribution cannot be decomposed.

we use a sampling technique and approximate the solution using samples obtained from the probability distribution.


The POS tags of such examples can be easily predicted, and they are used as global features in prediction of other examples.

this method uses unlabeled data in only the testing phase, and the training phase is the same as the case with no unlabeled data.


We treat all N-best phrase alignments equally.

the phrase alignments in Figure 4 are converted to the following three bilingual sentence pairs.


No translation, language, or distortion model probabilities are used as in earlier work on SMT.

our method, which employs less domain specific knowledge, is both simpler and more extensible than previous approaches.


Since it is impossible to define the complete vocabulary, we prepare a special token ÍÏ for unknown words and an unknown word spelling Ü ¼ ½ is predicted by the following character-based Ò-gram model after ÍÏ is predicted by Å Û Ò : where Ü ´ ¼µ and Ü ¼ •½ is a special symbol Ì.

when Û is outside of the vocabulary Ï, Nagata (1994) proposed a stochastic word segmenter based on a word Ò-gram model to solve the word segmentation problem.


¾ This is done by replacing the unknown word model based on the Japanese character set Å Ü Ò´Ü µ by a model based on the phonemic alphabet Å Ý Ò´Ý µ.

the candidate evaluation metric of a phoneme-to-text transcription (Mori et al., 1999) composed of the word Ò-gram model and the word-based pronunciation model is as follows: To cope with segmentation errors, the concept of stochastic segmentation is proposed (Mori and Takuma, 2004).


Word Ò-gram probability estimated from an SSC may not be as accurate as an LM estimated from a corpus segmented appropriately by hand.

we use the following interpolation technique: where À is history before Û , È × is the probability estimated from a segmented corpus × , and È Ö is the probability estimated by our method from a raw corpus Ö .


In the experiments, the word bi-gram model in our phoneme-to-text transcription system is combined with word bi-gram probabilities estimated from an SSC.

the phoneme-to-text transcription system of our new framework refers to the following LM to measure the likelihood of word sequences: where Ë Ö is the set of all subsequences appearing in the SSC.


To the contrary, using our method È´ µ ¼ when the sequence " " appears in the SSC at least once.

the sequence is enumerated as a candidate word.


The main difference between the models is the LM part.

the accuracy increase is yielded by the LM improvements.


Placing an object (d) beside (b) appears to interfere with the appropriateness of using a proximal relation to locate (c) relative to (b), even though the absolute distance between (c) and (b) has not changed.

there is empirical evidence for several contextual factors determining the applicability of a proximal description.


To ensure replicability of results, we assume perfect (i.e., OTC-supplied) events, times, and links.

we can consider TLINK inference as the following classification problem: given an ordered pair of elements X and Y, where X and Y are events or times which the human has related temporally via a TLINK, the classifier has to assign a label in RelTypes.


The contiguous phrase-prefixed form generates English in left-to-right order.

a decoder can easily hypothesize a derivation tree integrated with a ngram language model even with higher order.


With rules 10 and 11, the non-terminal X 1 behaves as a place holder where certain number of foreign words are skipped.

those rules realize a window size constraint used in many phrasebased models (Koehn et al., 2003).


Although there has been a fair amount of work employing linguistically sophisticated analysis of candidate items (e.g., on CE by Lin (1998) and Lin (1999) as well as on ATR by Daille (1996), Jacquemin (1999), and Jacquemin (2001)), these approaches are limited by the difficulty to port grammatical specifications to other domains (in the case of ATR) or by the error-proneness of full general-language parsers (in the case of CE).

most recent approaches in both areas have backed off to more shallow linguistic filtering techniques, such as POS tagging and phrase chunking (e.g., Frantzi et al.


3 We use the notation C mn ij to denote that i and j are (m, n)-cousins.

coordinate terms are (1, 1)-cousins; technically the hypernym relation may also be seen as a specific case of this representation; an immediate parent in the hypernym hierarchy is a (1, 0)-cousin, and the k-th ancestor is a (k, 0)-cousin.


Moreover, the in-between word features and the entity-related features used in the feature-based methods are also captured by the tree kernel and the entity kernel, respectively.

our method has the potential of effectively capturing not only most of the previous flat features but also the useful syntactic structure features.


Collins and Duffy (2001) report that in practice, running time for the parse tree kernel is more close to linear (O (|N 1 |+|N 2 |), rather than O(|N 1 |*|N 2 | ).

using the PC with Intel P4 3.0G CPU and 2G RAM, our system only takes about 110 minutes and 30 minutes to do training on the ACE 2003 (~77k training instances) and 2004 (~33k training instances) data, respectively.


As you can see in the comparison between {de, wo} and {de, de}, the probability becomes lower when multiple same cases are included.

the probability can reflect the constraint that multiple case elements of the same particle tend not to modify a verb.


As shown by previous work (Cogdill and Moore, 1997;De Groote and Dorsch, 2003), citations from the MEDLINE database (maintained by the U.S. National Library of Medicine) serve as a good source of clinical evidence.

of these findings, our work focuses on MEDLINE abstracts as the source for answers.


On the contrary, properties such as "having the property of doing an action" are less explicit, as they depend more strongly on the context of sentences.

properties useful to derive entailment relations among verbs are more difficult to find.


The probability of a parse tree T is the sum of the probabilities of its distinct derivations.

let t id be the i-th subtree in the derivation d that produces tree T, then the probability of T is given by DOP1 considers counts of subtrees of a wide range of sizes: everything from counts of singlelevel rules to entire trees is taken into account to compute the most probable parse tree of a sentence.


Unlike that work, all of our induction methods produce trees labeled with symbols which are identified with treebank categories.

we also report labeled precision, recall, and F 1 , still ignoring brackets of size 1.


As to build candidate sentence sets for testing, we retrieve all of the sentences from relevant documents in judgements and keep those which contain at least one question key word.

each question has at least one proper candidate sentence which contains proper answer in its candidate sentence set.


Furthermore, the method is especially effective for difficult questions, for which NER may not help.

it may be used to further enhance state-of-the-art QA systems even if they have a good NER.


Fig 2): while it it possible that strings such as "we" and "in this paper" occur more often in the context of a given cue phrase, they also occur in many other places in the paper where the cue phrase is not present.

it is hard to define clear negative contexts for our task.


not yet included in FN) are especially problematic since there is no training data available.

we should rely on a semantic context alternative to the frame (Giuglea and Moschitti, 2004).


The main advantage of using Levin classes comes from the fact that other resources like PB and the VN lexicon contain this kind of information.

we can train an ILC classifier also on the PB corpus, considerably increasing the verb knowledge base at our disposal.


Although alternations are chosen as the primary means for identifying the classes, additional properties related to subcategorization, morphology and extended meanings of verbs are taken into account as well.

from a syntactic point of view, the verbs in one Levin class have a regular behavior, different from the verbs pertaining to other classes.


In doing this we have applied the simplifying assumption that a frame has a unique corresponding Levin class.

we have created a one-to-many mapping between the ILCs and the frames.


In order to assign a VN class to a frame, we have to verify that the verbs belonging to the FN frame participate in the same diathesis alternation criteria used to define the VN class.

the pairs F, C formed in step 1 of the mapping algorithm have to undergo a validation step that verifies the similarity between the enclosed FN frame and VN class.


Only 501 verb senses are in common between the two corpora which means 13.64% of PB and 21.31% of FN.

by training an ILC classifier on both PB and FN we extend the number of available verb senses to 5,522.


Leaf nodes are very specific and are only used to index video content.

the usefulness of any keyword phrase for providing access to the digital collection is directly related to the concept's position in the thesaurus hierarchy.


Some keyword phrases contain words with high translation value, but the keyword phrase itself has low thesaurus value.

the value gained by translating any given phrase is more accurately estimated by the total value of any untranslated words it contains.


A few words about additional characteristics of this approach: note that it is greedy and biased toward longer keyword phrases.

some words may be translated more than once because they appear in more than one keyword phrase with high translation value.


A pair of lexical items is selected as a candidate only if there is a syntactic relation holding between the two items (one being the head of the current parse structure, and the other the lexical head of its specifier/complement).

the criterion we employ for candidate selection is the syntactic proximity, as opposed to the linear proximity used by traditional, window-based methods.


But there is no evidence sustaining that the same holds for other languages, like German or the Romance ones that exhibit freer word order.

as window-based systems miss the 'long-distance' pairs, their recall is presumably lower than that of parse-based systems.


The precision curve for the window method shows a more rapid degradation than it does for the other.

we can conclude that parsing is especially advantageous if one investigates more that the first hundred results (as it seems reasonable for large extraction experiments).


Figure 3 contains a sample corpus from which four rules can be extracted: From Equation 4, the probabilities of r 3 and r 4 must be 1, and those of r 1 and r 2 must sum to 1.

the total probability mass, which is distributed across two possible output strings a'b'c' and b'a'c', is: It is relatively easy to prove that the probabilities of all derivations that correspond to a given decomposition λ i ∈ Λ sum to 1 (the proof is omitted due to constraints on space).


The study considered word alignments both in isolation and in combination with independently generated parse trees for one or both sentences in each pair.

the study is relevant to finite-state phrase-based models that use no parse trees (Koehn et al., 2003), tree-tostring models that rely on one parse tree (Yamada and Knight, 2001), and tree-to-tree models that rely on two parse trees (Groves et al., 2004, e.g.).


(p. 307) As explained in Section 3, we modified all unreliable alignments so that they cannot increase the complexity measure.

we arrived at complexity measurements that were underestimates, but reliably so.


Even for such a modest value of n the number of parameters is still tremendous due to the large vocabulary size.

direct maximum-likelihood parameter fitting severely overfits to the training data, and smoothing methods are indispensible for proper training of n-gram models.


(Goldwater et al., 2006) has independently noted the correspondence between the hierarchical Pitman-Yor language model and interpolated Kneser-Ney, and conjectured improved performance in the hierarchical Pitman-Yor language model, which we verify here.

the contributions of this paper are threefold: in proposing a langauge model with excellent performance and the accompanying advantages of Bayesian probabilistic models, in proposing a novel and efficient inference scheme for the model, and in establishing the direct correspondence between interpolated Kneser-Ney and the Bayesian approach.


We assume that mappings between chat terms and their standard Chinese counterparts are independent of each other.

chat term normalization probability can be calculated as follows.


We assume that the phonetic mapping models depend merely on the current observation.

the phonetic mapping probability is calculated as follows.


However, an uncom-pressed language model is usually too large for practical use since all realistic applications have memory constraints.

language model pruning techniques are used to produce smaller models.


If we remove a seen bigram from the model, we can still yield a bigram probability for it, by regarding it as an unseen bigram.

we can reduce the number of bigram probabilities explicitly stored in the model.


In addition, similar deductions can be conducted to the second bracket in equation (11).

we have: Note that d(S;Λ,Г) approximately indicates the likelihood that S will be incorrectly segmented, so Δd(S;w x w y ) represents the performance im-provement caused by adding w x w y .


Thus, we have: Note that d(S;Λ,Г) approximately indicates the likelihood that S will be incorrectly segmented, so Δd(S;w x w y ) represents the performance im-provement caused by adding w x w y .

"importance" of bigram w x w y on S is computed as: Case 2: and Here, it is just contrary to case 1.


the misclassification function in equation 8does not represent the likelihood that S will be incorrectly segmented.


Thus, the misclassification function in equation 8does not represent the likelihood that S will be incorrectly segmented.

variation of the misclassification function in equation 13can not be used to measure the "importance" of a bigram.


Generally, adding more bigrams at one time will lead to more negative impacts.

it is expected that models produced by step-by-step growing algorithm may achieve better performance than growing algorithm, and smaller step size will lead to even better performance.


Total 36 different "Chien-Ming Wang"s occur more than once.


These expressions in MEDLINE were automatically annotated with event types.

we obtained semantically annotated MEDLINE.


This property makes it more theoretically sound to be used as error model probability in the Bayesian framework of the source channel model.

it can be applied and evaluated independently.


From the results we can see that after the number of training samples reaches 600 there are only subtle changes in accuracy and recall.

basically it can be concluded that 2,000 samples are sufficient to train a maximum entropy model with the current feature sets.


Two, the context used during the generation of a landmark description is always a subset of the context used for a target (as the target, its distractors and the other objects in the domain that do not stand in relation to the target or distractors under the relation being considered are excluded).

the framework avoids the issue of infinite recusion.


This difficulty in generation, however, does not mean the choice of case markers is insignificant: when a generated sentence contains mistakes in grammatical elements, they often lead to severe unintelligibility, sometimes resulting in a different semantic interpretation from the intended one.

having a model that makes reasonable predictions about which case marker to generate given the content words of a sentence, is expected to help MT and generation in general, particularly when the source (or native) and the target languages are morphologically divergent.


Unlike other focus particles such as shika and mo, wa does not translate into any word in English, which makes it difficult to generate by using the information from the source language.

in addition to the 10 true case markers, we also included wa as a case marker in our study.


Thus there are seven secondary targets: niwa, karawa, towa, dewa, ewa, madewa, yoriwa.

we have in total 18 case particles to assign to phrases.


The huge number of documents, however, make the task extremely daunting.

an automated test of different perspectives will be very valuable to information analysts.


The KL divergence of the document collection pairs in the DP condition fall mostly in the middle range, and is well separated from the high KL divergence of the pairs in DT condition and from the low KL divergence of the pairs in SP and ST conditions.

by simply calculating the KL divergence of a document collection pair, we can reliably predict that they are written from different perspectives if the value of KL divergence falls in the middle range, from different topics if the value is very large, from the same topic or perspective if the value is very small.


But they are not subjective due to alarm and boil, but rather to punctuation, sentence forms, and other words in the sentence.

classifying a sense as O means that, when the sense is used in a text or conversation, we do not expect it to express subjectivity and, if the phrase or sentence containing it is subjective, the subjectivity is due to something else.


If we consider these cases to be borderline and exclude them from the study, percent agreement increases to 95% and κ rises to 0.90.

annotator agreement is especially high when both are certain.


Note that the annotations in the MPQA corpus are for subjective expressions in context.

the data is somewhat noisy for our task, because, as discussed in Section 3, objective senses may appear in subjective expressions.


We call this entity the inverted or pivot term.

for the question: (1) "What was the capital of Germany in 1985?"


However, in the context of our system, we are comparing the pivot term from the original question to the answers to the inverted questions, and failure here will directly impact validation and hence the usefulness of the entire approach.

we have identified the need for a component whose sole purpose is to establish the equivalence, or generally the kind of relationship, between two terms.


Biterm language models are similar to bigram language models except that the constraint of order in terms is relaxed.

a document containing information retrieval and a document containing retrieval (of) information will be assigned the same generation probability.


They all generate the class of mildly context-sensitive languages (MCSL), all members of which have recognition algorithms with time complexity O(n 6 ) (Vijay-Shanker and Weir, 1993;Satta, 1994).

1 of the weak equivalence of four independently developed (and linguistically motivated) extensions of CFG, the class MCSL is considered to be linguistically meaningful, a natural class of languages for characterizing natural languages.


A named entity recognition system modeling this structure would encourage all the occurrences of the token sequence to the same entity type, thereby sharing evidence among them.

if the system has strong evidence about the label of a given token sequence, but is relatively unsure about the label to be assigned to another occurrence of a similar token sequence, the system can gain significantly by using the information about the label assigned to the former occurrence, to label the relatively ambiguous token sequence, leading to accuracy improvements.


As a result of this, while there would be several cases in which the basic sequence model would be uncertain about labels of entity subsequences but relatively certain about labels of token supersequences, the converse is very unlikely.

it is difficult to profit from labels of entity subsequences while labeling entity sequences.


For example, "Taxi" becomes " 的士 /Di-Shi/" in China and " 德士 /De-Shi/" in Singapore.

the adequacy of a transliteration entry could be judged by its popularity and its conformity with the translation-by-sound principle.


A Chinese word consists of a sequence of characters, phonetically a sequence of syllables.

in first E-C transliteration, it is a natural choice to syllabify an English word by converting its phoneme sequence into a sequence of Chinese-like syllables, and then convert it into a sequence of Chinese characters.


During Web crawling, most of the search engines use various strategies to prevent spamming and one of fundamental tasks is to remove the duplicated Web pages.

we assume that the bilingual snippets are all unique.


We also propose to use Support Vector Machines to filter out noisy instances that indicates no specific time period.

of experiments, the proposed method achieved 0.864 of accuracy and outperformed other methods.


This bias would affect our classification process.

we propose a method for tackling the problem.


Then "cereal" will be a time-associated word of morning.

we can use "cereal" as a clue of timeslot classification.


1 The aim of this study is time-slot classification of events.

we treat only sentences expressing an event.


Second, there are no time-associated words in the sentences categorized into "time-unknown".

the feature distribution of time-unknown time-slot sentences is remarkably different from the others.


We prepare another classifier for filtering time-unknown sentences before NB+EM process for that purpose.

we propose a classification method in 2 steps (Method A).


Furthermore, the accuracy of the proposed method 'NB+EM' exceeds that of the 'NB' by 11%.

we figure out that using unlabeled data improves the performance of our time-slot classification.


PerfMatch, on the other hand, cannot handle one-to-many relationships, acts "cautiously" and aligns the English NP to a dummy node, leading to a recall error.

even though EdgeCover's analysis is partly right, it will come out worse than PerfMatch, given the current dataset and evaluation method.


This could happen in our experiments on ordering MT output sentences.

in this section we aim to evaluate the usefulness of the constraint in both settings: reference sentences with projective dependency trees, and MT output sentences with possibly non-projective dependency trees.


We also conclude that the constraints imposed by the target dependency trees constructed by our baseline MT system are very informative as well, even though the trees are not necessarily projective with respect to the best order.

the projectivity constraint with respect to a reasonably good target dependency tree is useful for addressing the search and modeling problems for MT ordering.


Constituents that project onto non-contiguous spans of the source sentence do not yield transducer rules themselves, and can only be incorporated into larger transducer rules.

if the word alignment of a sentence pair does not respect the constituent structure of the target sentence, then the minimal translation units must span large tree fragments, which do not generalize well.


(2006) to initialize the model parameters, we achieve an AER superior to the GIZA++ implementation of IBM model 4 (Och and Ney, 2003) and a reduction of 56.3% in aligned interior nodes, a measure of agreement between alignments and parses.

our alignments yield more rules, which better match those we would extract had we used manual alignments.


However, no technique has yet been shown to robustly extract smaller component rules from a large transducer rule.

for the purpose of maximizing the coverage of the extracted translation model, we prefer to extract many small, minimal rules and generate larger rules via composition.


All of the component distributions of our lexical and distortion models are multinomials.

upon assuming these expectations as values for the hidden alignment vectors, we maximize likelihood of the training data simply by computing relative frequencies for each component multinomial.


The selection step discards bad machine translations and reinforces phrases of high quality.

the probabilities of lowquality phrase pairs, such as noise in the table or overly confident singletons, degrade.


Each attribute has a type (its name) and a single value for a given example.

attribute-value vectors have the same expressiveness as propositional formalisms, that is, they only allow the representation of atomic propositions and constants.


Though comprehensive in scale and widely used, WordNet suffers from an obvious structural rigidity in which concepts are either entirely within a category or entirely outside a category: no gradation of category membership is allowed, and no contextual factors are brought to bear on criteria for membership.

a gun is always a weapon in WordNet while an axe is never so, despite the uses (sporting or murderous) to which each can be put.


There is a single variable (we will denote this variable "m") corresponding to whether the implication holds.

m = 1 means that f 1 implies f 2 and m = 0 means that it does not.


We believe this to be an appropriate noise model because it models the fact that the majority of information for a single language is from a single source.

if there is an error in the database, it is more likely that other errors will be for the same languages.


However DLMs in previous studies have been restricted to specific applications.

the model cannot be used for other applications.


However in contrast to the support set in SVMs, an example is added to the active set every time the online algorithm makes a prediction mistake or when its confidence in a prediction is inadequately low.

the active set can increase in size significantly and thus the total computational cost becomes proportional to the square of the number of training examples.


For a non-binary feature X, its value x is normalized by z-score, , where mean(x) is the empirical mean of X and var(X) is the variance of X.

each sentence is represented by a vector.


In practice, the OOV increase caused by synthesis is much larger, as languages can have thousands of different word forms per word that are caused by addition of processes of word formation followed by inflections.

the OOV problem in ASR has been most pronounced in languages with much synthesis, regardless of the amount of fusion.


The increase in the number of states aggravates sparse-data problems.

we implemented another variant (v2) which uses the same set of states (with A and N flags), but with the transition probabilities of the original model, which did not enforce the constraints.


Manual annotation is only available for a small number of words.

only automatically annotated morphological information can scale up to real applications.


The benefit of using morphological preprocessing is also affected by the algorithm that is used for g2p conversion.

we also evaluated the relative improvement of morphological annotation when using a decision tree for g2p conversion.


For an inventory of size N let there be p f consonants for which a particular feature f (where f is assumed to be boolean in nature) is present and q f other consonants for which the same is absent.

the probability that a particular consonant chosen uniformly at random from this inventory has the feature f is N and the probability that the consonant lacks the feature f is ).


Let there be 317 bins corresponding to each consonant inventory L. A bin corresponding to an inventory L is packed with s L consonants chosen uniformly at random (without repetition) from the pool of 541 available consonants.

the consonant inventories of the 317 languages corresponding to the bins are generated.


From these comparable articles across the paired languages, the same named entities are expected to be found.

from the named entities in an English newspaper, the method would find transliteration correspondences in comparable texts in other languages.


For example, Chinese does not allow consonant clusters or coda consonants except [n,N], and this results in deletion, substitution of consonants or insertion of vowels.

while a syllable initial /d/ may surface as in Baghdad 巴格达 ba-ge-da, note that the syllable final /d/ is not represented.


Indeed, for some rarely spoken languages, it is practically impossible to find enough training data.

the proposed method aims to obtain comparable performance with little training data.


The method can be trained using a small amount of training data, and the performance decreases only by a small degree when it is trained with a language different from the test data.

this method is extremely useful for underrepresented languages for which training data is difficult to find.


To test the applicability to languages for which training data is not available, we also investigated the use of models trained on language pairs different from the target language pair.

for each test language pair, we evaluated the performance of models trained on each of the other language pairs.


The Latin-scripted personal names are always assumed to homogeneously follow the English phonic rules in automatic transliteration (Li et al., 2004).

the same transliteration model is applied to all the names indiscriminatively.


A Latin-scripted personal name is written in letters, which represent the pronunciations closely, whereas each Chinese character represents not only the syllables, but also the semantic associations.

character rendering is a vital issue in transliteration.


We report accuracy only on words that appear in our lexicons.

punctuation, English words occurring in the target sentence, and words with unknown lemmas are excluded from the evaluation.


In such cases human translators usually apply structural and lexical transformations, for instance changing the descriptors' POS and/or replacing them with near-synonyms which fit together in the context of a TL sentence (Munday, 2001: 57-58).

a structural transformation of плохо отремонтированные (badly repaired) may give in poor repair while a lexical transformation of недостает самого необходимого ([it] is missing the most necessary) gives lacking basic essentials.